The present invention relates generally to a multiple model (MM) radar tracking filter and, more specifically, to a MM radar tracking filter that does not employ a Markov Switching Matrix (MSM). A method for processing information received by a radar system is also disclosed.
Traditional multiple model (MM) radar tracking filter structures use Bayesian techniques to adaptively switch between different motion models implemented in the filter structure. These logic designs typically rely upon a predefined Markov Switching Matrix (MSM), whose entries are selected in a more or less ad hoc manner.
One of the striking features of all multiple model (MM) filter switching logic designs is that they invariably place reliance upon a predefined Markov Switching Matrix (MSM), Π, as illustrated in FIG. 1. This N×N matrix, where N equals the number of dynamic models in the filter bank, consists of switching probabilities, pij, for i, j=1 . . . N , having the following characteristics:                1. The diagonal element, pij, represents the conditional probability that the system remains in state i during the next cycle given that it is currently in state i;        2. The off diagonal element, pij, represents the conditional probability that the system will transition into state j during the next cycle given that it is currently in state i; and        3. All the probabilities of a given row must sum to unity indicating that the system must either remain in the current state or transition to some other state.        
No “optimum” method has been devised to populate the matrix, Π. While it is generally agreed that the diagonal values are “close to unity” and the off-diagonal elements are “small” subject to the constraint that the sum of all the elements of a row is unity, no specific rules have yet been devised for uniquely determining specific numerical values for a given tactical scenario. It is strictly up to each designer to select values using some ad hoc technique. When a set of values has been selected, numerous computer simulations are run and results compared with runs from other combinations of values. The set of values that produces the “best” results are then selected as the final design values. However, a set of values that may be “best” for one type of target may not be “best” for a different type of target. Thus, it is impossible to select a set of values that is “best” for all targets likely to be encountered.
The MSM has a very significant impact on how rapidly the switching mechanism detects and then responds to a maneuver by the target. A poorly selected set of values produces a sluggish filter response to a target maneuver. The reshuffling of the weights can be delayed if improper values are used in the MSM. As a result, significant filter lags develop, target tracks may be lost, and incorrect track correlations will follow.
What distinguishes a superior filter design from a poor filter design is the speed with which the switching logic detects and then responds to a target maneuver by reshuffling the weights to match the new target dynamic configuration. Since most MM filter designs incorporate a MSM as part of their switching logic, this matrix, whose values are selected in a generally ad hoc manner, has a significant impact on the response time of the switching logic to a sudden target maneuver. It will be appreciated that there is no “optimum” method for selecting values with which to populate this matrix. A set of values that may provide “good” tracking performance against a specific target type may not yield good track performance for a different target. Since one cannot know in advance what target type is going to be encountered in a given scenario, the filter designer is faced with a design dilemma.
In spite of this, the MM filter structure has won wide acceptance within the academic tracking community and system developers in other fields of endeavor. For example, U.S. Pat. No. 5,325,098 to Blair et al. discloses an interacting multiple bias model filter system for tracking and maneuvering targets. However, the system utilizes Markovian switching coefficients for its logic. Moreover, U.S. Pat. No. 5,479,360 to Seif et al. discloses a method of target passive ranging that does not require ownship to maneuver. In the latter patent, multiple Kalman filters feed a model probability update circuit. The function pst is an assumed Markov model transition or switching probability function whose value provides the probability of jumping or changing from model s at time K−1 to model t at time K. The values of the model transition probabilities are determined as part of the overall system design, analogously to the choice of values for the initial values of the predetermined model parameters.
Furthermore, U.S. Published App. No. 20020177951 to Syrjarinne discloses a two stage Interacting Multiple Model (IMM) for use in a global positioning system. More specifically, the '951 published application discloses a bank of predictive filters k, disposed in parallel, wherein estimates, covariance, and likelihood values are determined for each filter. As shown in FIG. 2 of the published application, the values are applied to a mixing unit and a combinational circuit. While the reference indicates that the outputs of the k Kalman filters are weighed, the weighting mechanism is not expressly defined. However, one of ordinary skill in the art will appreciate that the switching/weighting logic follows the MSM methodology.
Consequently, pressure is mounting to incorporate this filter structure into tactical tracking systems, e.g., radar tracking systems. Unfortunately, the ad hoc nature of selecting values for the MSM makes it difficult to predict, with any degree of certainty, what performance statistics can be anticipated for any given filter design. This represents a stumbling block for implementing these filters into tactical tracking systems.
What is needed is an alternative multiple model switching logic filter and operating method therefor that does not employ a Markov Switching Matrix.